A New Standard for Assessing the Performance of High Contrast Imaging Systems
Bibliographic record
Abstract
As planning for the next generation of high contrast imaging instruments (e.g., WFIRST, HabEx, and LUVOIR, TMT-PFI, EELT-EPICS) matures and second-generation ground-based extreme adaptive optics facilities (e.g., VLT-SPHERE, Gemini-GPI) finish their principal surveys, it is imperative that the performance of different designs, post-processing algorithms, observing strategies, and survey results be compared in a consistent, statistically robust framework. In this paper, we argue that the current industry standard for such comparisons—the contrast curve—falls short of this mandate. We propose a new figure of merit, the “performance map,” that incorporates three fundamental concepts in signal detection theory: the true positive fraction, the false positive fraction, and the detection threshold. By supplying a theoretical basis and recipe for generating the performance map, we hope to encourage the widespread adoption of this new metric across subfields in exoplanet imaging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".